Google Research

3 posts

MilleMiglia: A realistic instance generator for middle-mile logistics research.google
MilleMiglia is an open‑source C++ generator that creates realistic, privacy‑preserving middle‑mile logistics instances (space‑time graphs with fixed schedules, throughput limits, and synchronization constraints). It uses data‑driven spatial, demand, and rotation distributions, serializes with protobuf, and ships small files for small‑toy to continent‑scale problems, enabling both exact/heuristic…
Google Researchresearch.google3d7 min
The future of practice: Enabling teachers to create learning interactives with generative UI research.google
Google Research demonstrates a generative‑UI system that lets teachers author interactive, game‑style learning modules. The pipeline uses pedagogical guardrails, iterative self‑correcting loops (including automated solvability checks), and teacher vetting. A pilot library of 30+ STEM interactives is publicly available, and early teacher studies report good‑to‑excellent ratings.
Google Researchresearch.google4d8 minHN4
Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train research.google
Retrieve-for-Train addresses LLM inference bottlenecks in complex AI search by using offline reinforcement learning to train a lightweight diffusion model. This enables efficient, single-pass generation of diverse, property-aligned sub-queries, bypassing slow autoregressive reasoning.
Google Researchresearch.google6d8 minHN2

Google Research

MilleMiglia: A realistic instance generator for middle-mile logisticsresearch.google

The future of practice: Enabling teachers to create learning interactives with generative UIresearch.google

Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Trainresearch.google

MilleMiglia: A realistic instance generator for middle-mile logistics research.google

The future of practice: Enabling teachers to create learning interactives with generative UI research.google

Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train research.google